Integrated sensing and communications (ISAC) has emerged as a key enabler for 6G and beyond. However, ISAC systems face significant challenges, including the sensing function that introduces interference and degrades communication performance, as well as high sensing power consumption that reduces overall communication efficiency, particularly in complex urban environments. To address these issues, we propose a reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) ISAC system, where a RIS enhances connectivity for users in localized coverage gaps. We formulate and study two optimization problems: i) maximizing system sum spectral efficiency and ii) maximizing global energy efficiency, by jointly optimizing transmit precoding, subcarrier allocation, and RIS phase shifts under power, quality of service, and sensing accuracy constraints. These problems are classified as mixed-integer nonlinear programs, which are generally difficult to solve optimally. To tackle this, we develop efficient iterative algorithms leveraging successive convex approximation, alternating optimization, Riemannian manifolds, and Dinkelbach’s method to obtain at least locally optimal solutions. Simulation results validate the effectiveness of the proposed designs, demonstrating their superiority over benchmark schemes, achieving up to 40% higher spectral efficiency and up to 60% improvement in energy efficiency compared to conventional overlap and random-phase approaches.

In this work, we consider a typical scenario in a harsh urban propagation environment which is typical for a smart city scenario where multiple reconfigurable intelligent surfaces (RISs) are deployed in different hotspot areas to overcome signal blockage between the base station and users. Our goal is to ensure uninterrupted service availability to users in different hotspot areas regardless of their location. Consistent service availability can be achieved by guaranteeing that each RIS deployed in a hotspot area can support a certain number of users. This plays a critical role in smart city applications in the context of emergency communications and ubiquitous connectivity since the design ensures service availability to as many users as possible in all relevant locations. Taking into consideration the challenges in obtaining channel state information (CSI) given the passive nature of RIS and dynamic environments, we formulate a robust fairness problem to maximize the minimum expected number of served users in proximity to each RIS while considering the available transmit power and the worst-case quality of service (QoS) constraints within the bounded CSI error model framework. The resulting problem is a mixed integer non-convex program which is highly coupled and challenging to solve in polynomial time. Thus, we resort to binary variable relaxation, convex approximation techniques, and alternating optimization to tackle the problem. Additionally, we handle the semi-infinite uncertainty constraints by employing the S-procedure and general sign-definiteness. Simulation results demonstrate the effectiveness of the proposed design in obtaining consistent and reliable service in different hotspot areas compared to the relevant benchmark schemes. In addition, the proposed design shows flexibility in serving users with their target QoS given different channel uncertainty levels.

In this paper, we study the resource allocation problem in multi-RIS-aided outdoor-to-indoor communications. Specifically, we aim to provide geographical fairness to ensure that users in different hotspot areas in a smart city can be served regardless of their location. We consider a scenario where RISs are deployed to extend coverage to indoor users in different buildings where there is limited network accessibility. This design is crucial in smart cities in the context of emergency communication and ubiquitous connectivity since it ensures service availability to as many users as possible independently of the locations. Thus, to achieve geographical fairness, we formulate a max-min fairness problem to maximize the minimum number of users served by each RIS by jointly optimizing the active precoding and RIS-based beamforming subject to power and quality of service constraints. The geographical location of users is directly linked to the RIS which means that users are served by the RIS closest to them. In this case, we ensure that a certain number of users can be supported by each RIS. The formulated problem is a mixed integer nonlinear program, which is challenging to solve directly using methods of convex optimization. Accordingly, we propose an efficient successive convex approximation-based alternating optimization algorithm to tackle the complexity of the formulated problem. The presented results show the performance gain of the proposed design in providing geographical fairness compared to the relevant benchmark schemes.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

In this paper, we tackle the problem of joint symbol level precoding (SLP) and reconfigurable intelligent surface (RIS) phase shift design with constellation rotation in the finite block length regime. We aim to increase energy efficiency by minimizing the total transmit power while satisfying the quality of service constraints. The total power consumption can be significantly minimized through the exploitation of multiuser interference by symbol level precoding and by the intelligent manipulation of the propagation environment using reconfigurable intelligent surfaces. In addition, the constellation rotation per user contributes to energy efficiency by aligning the symbol phases of the users, thus improving the utilization of constructive interference. The formulated power minimization problem is non-convex and correspondingly difficult to solve directly. Hence, we employ an alternating optimization algorithm to tackle the joint optimization of SLP and RIS phase shift design. The optimal phase of each user’s constellation rotation is obtained via an exhaustive search algorithm. Through Monte-Carlo simulation results, we demonstrate that the proposed solution yields substantial power minimization as compared to conventional SLP, zero forcing precoding with RIS as well as the benchmark schemes without RIS.

Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.